Skyhive Technologies Holdings Inc.

Skyhive Technologies Inc.

Rupert Cosulich

Mohan Reddy

Sergey Bukharov

Yuri Yerastov

Sean Thomas Hinton

Mykhailo Timonin

Techniques are described for automatically generating job descriptions. Training data is used to define job categories, job sections, and selective phrases for each job category and job section. In response to receiving request to generate a job description, a job category or additional information is derived from the request. Such information is used to populate existing templates for a job description based on a skill knowledge graph, which connects skill names based on hypernym trees and enables enhancement of a job description with better or broader coverage.

Job description generation based on machine learning

Techniques are described for automatically generating job descriptions. Job categories are identified for the documents in the job description dataset based on clustering the documents. Sections within each document in the job description dataset are automatically identified, and each identified section is automatically classified. Each type of section is analyzed independently of the other section types using natural language processing, which allows automatic generation of each section of a new job description based on the section-specific data derived from the dataset. Section- and category-specific selective phrases are identified within the job description dataset, and embeddings are generated for the identified selective phrases. A request to generate a new job description for a particular category is received. For each section of new job description, the selective phrase embeddings for the section and for the identified category are used to identify phrases to include within the section of the job description.

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Vancouver, Canada

Rupert Cosulich